Many new consumer services offered to users of mobile devices or internet users rely on the collection of user data from the users. These data can be used to personalize services or to offer content- or context-dependent services to the user. In order to receive services or to improve the perceived quality of the service, users are often required to submit personal information in electronic form to the other party, the other party meaning the service or another user. Revealing personal data, however, has privacy implications for the user. Privacy has been a hot topic, especially in social media applications like Facebook. In order to decide, whether the user is willing to reveal personal information, the user has to make a trade-off between three factors: trust, privacy risk and benefit.
A user's trust to the other party measures the level of confidence of the user that the other party will handle the user's information according to the declared or agreed upon policies. If a user's trust to a party is high, the user may be willing to reveal more and more sensitive data than if the level of trust would be low.
An existing solution for determining privacy risk concerning personal information has been introduced, e.g., in L. Sweeney. k-Anonymity: a model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10 (7), 2002.
Also the prior art contains frequent itemset mining algorithms, such as Apriori, which has been introduced, e.g., in Rakesh Agrawal, Heikki Mannila, Ramakrishnan Srikant, Hannu Toivonen and A. Inkeri Verkamo “Fast Discovery of Association Rules” Advances in knowledge discovery and data mining, pp. 307-328, American Association for Artificial Intelligence, Menlo Park, Calif., USA, 1996. ISBN 0-262-56097-6.
The trustworthiness of the other party and the perceived benefits that come in exchange for revealing personal data have to be weighed against the privacy risks that come as a consequence of revealing user information. This is difficult since often the privacy risks related to user data are rather complex to understand and evaluate. Users tend also to underestimate the long-term privacy risks in favor of short-term benefits.